Object Contour Extraction Based on Intensity and Texture Information

In this paper, we propose a new method to extract object contour in a given gray-level image, whose foreground and background are statistically homogeneous and different. Firstly, the image for contour extraction is decomposed by discrete wavelet transform, and the high-pass and low-pass components are used to form intensity and texture energy respectively. Secondly, a minimal partition function, which combines intensity, texture and contour length energy, is made to model the contour extraction problem. Finally, the model is formulated in terms of level set function to obtain a numerical solution. Experiments have been performed on synthetic and remote-sensing images, and the results demonstrated that our method can adaptively use intensity and texture information to accurately extract object contour.